WU Bajwa, A Sayeed, R Nowak, Sparse multipath channels: modeling and estimation, in Proceedings of the 13th IEEE Digital Signal Processing Workshop (Marco Island). 4-7 Jan 2009W. Bajwa, A. Sayeed, and R. Nowak, "Sparse multipath channels: Modeling and estimation," in Proc. Digital ...
To overcome this difficulty, we introduce sparse modeling into the estimation of a strain-rate field. Specifically, we impose a sparsity constraint as well as the smoothness constraint on strain rates as prior information, which are expressed by the L1-norm and the L2-norm of the second-order...
However, this assumption is questionable as it is hard to hold in most of cases and none of existing methods may provide consistent estimation and accurate model prediction in nons-parse scenarios. In this paper, we propose semiparametric re-modeling and inference when the linear regression model...
,2 are the largest and second largest intensities in the i th cycle, respectively. because 3dec [ 5 ] assigns the called base of i th cycle as the type with the largest intensity, the signal intensities such as x i ,1 and x i ,2 are crucial to the estimation of error probability....
Sparse modeling and model selection To avoid overfitting and to select a subset of significant features, we reduce the initial logistic regression model by imposing sparsity constraints. That is, we impose aL0orL1penalty to the log-likelihood function of the logistic models, and optimize the penali...
modeling approaches and their connections. We derive the estimation procedures with these priors using variational inference for fully Bayesian estimation. In addition, we discuss the differences between the proposed inference and deterministic inference approaches with these priors. Finally, we show the ...
2 Sparse representations and clustering 2.1 Sparse representations: An overview Sparse representations have become a key research topic with various applications in signal and data processing, e.g., denoising [36,45], modeling [46], restoration [47,48], compression [49,50], and even more [51...
The sparse attention patterns we introduced are only preliminary steps in the direction of efficient modeling of long sequences. We think exploring different patterns and combinations of sparsity is useful, and that learning sparse patterns is a particularly promising avenue of research for the next ...
In this study, sparse modeling is introduced for the estimation of propagation patterns in intracardiac atrial fibrillation (AF) signals. The estimation is based on the partial directed coherence function, derived from fitting a multivariate autoregressive model to the observed signal using least-squares...
Sorted L1 Penalized Estimation rsparse-regressionslopegeneralized-linear-models UpdatedMar 14, 2025 C++ Sequential adaptive elastic net (SAEN) approach, complex-valued LARS solver for weighted Lasso/elastic-net problems, and sparsity (or model) order detection with an application to single-snapshot sou...